GPT-5.3-Codex vs MiniMax M3
Updated October 21, 2026. 29 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick MiniMax M3 for coding: it scores 57.8 on the Vibe Coding Index against 29.5 for GPT-5.3-Codex.
Pick GPT-5.3-Codex when what matters is LiveCodeBench (Vals AI) or SWE-bench Verified (Vals AI), where it leads.
- Shared results
- 29
- GPT-5.3-Codex only
- 5
- MiniMax M3 only
- 87
- Like-for-like categories
- 6 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, four public coding benchmarks
MiniMax M3
MiniMax M3 scores 57.8 against 29.5.
1 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.3-Codex
GPT-5.3-Codex leads, 32.5 against 29.2.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only MiniMax M3 has a AA Agentic Index result, 29.5.
Directional only
Reasoning
GPQA Diamond
No call
Only MiniMax M3 has a GPQA Diamond result, 92.7%.
Directional only
Knowledge
MMLU Pro
No call
Only MiniMax M3 has a MMLU Pro result, 84.2%.
Directional only
Web design
Design Arena: websites
MiniMax M3
MiniMax M3 leads, 1257 against 1163.
Same board, both measured
Long documents
Listed context window
MiniMax M3
MiniMax M3 takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiniMax M3
GPT-5.3-Codex $0.0088, MiniMax M3 $0.0009 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
MiniMax M3
GPT-5.3-Codex $0.59, MiniMax M3 $0.56.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5.3-Codex 29.5 from 1 benchmark, MiniMax M3 57.8 from 2 benchmarks.
- Widest category
- Agentic work, 16.4 points apart on average over 6 shared benchmarks.
- Widest single gap
- ALE-Bench: GPT-5.3-Codex 1655.2 against MiniMax M3 640.0, 33.8 points apart on a 0 to 100 scale.
- Output price
- GPT-5.3-Codex $14.00 against MiniMax M3 $1.20 per million output tokens, 11.7x apart.
- Context window
- GPT-5.3-Codex takes 400K tokens, MiniMax M3 1.05M.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | GPT-5.3-Codex | Bars | MiniMax M3 | Ties |
|---|---|---|---|---|
| Coding | 5 | 4 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 5 | ||
| Reasoning | 1 | 1 | ||
| Web design | 0 | 8 | ||
| General | 1 | 1 | ||
| All shared boards | 10 | 19 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index54
| Benchmark | GPT-5.3-Codex | MiniMax M3 | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 70.9% | Only MiniMax M3 measured |
| LMArena WebDev | 1407 | 1482 | MiniMax M3 |
| AA Coding Index | Not measured | 58.6 | Only MiniMax M3 measured |
| LiveCodeBench (Vals AI) | 87.3% | 82.2% | GPT-5.3-Codex |
| SWE-bench Verified (Vals AI) | 78.0% | 75.0% | GPT-5.3-Codex |
| Terminal-Bench 4 (Vals AI) | Not measured | 1.0% | Only MiniMax M3 measured |
| Vibe Code Bench (Vals AI) | 61.8% | 47.6% | GPT-5.3-Codex |
| Code migration (Vals AI) | Not measured | 19.9% | Only MiniMax M3 measured |
| IOI (Vals AI) | 53.8% | Not measured | Only GPT-5.3-Codex measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 53.6% | Only MiniMax M3 measured |
| SWE-bench Multimodal (Vals AI) | Not measured | 33.3% | Only MiniMax M3 measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 9.2% | Only MiniMax M3 measured |
| SkillsBench (Vals AI) | Not measured | 51.5% | Only MiniMax M3 measured |
| CyberBench (Vals AI) | Not measured | 65.0% | Only MiniMax M3 measured |
| SciCode | Not measured | 47.1% | Only MiniMax M3 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 53.0% | 42.4% | GPT-5.3-Codex |
| Terminal-Bench | 78.4% | Not measured | Only GPT-5.3-Codex measured |
| FrontierCode | Not measured | 14.7% | Only MiniMax M3 measured |
| WeirdML | 79.3% | Not measured | Only GPT-5.3-Codex measured |
| ALE-Bench | 1655.2 | 640.0 | GPT-5.3-Codex |
| LMArena Text: coding | Not measured | 1470 | Only MiniMax M3 measured |
| LMArena Text: software and IT | Not measured | 1466 | Only MiniMax M3 measured |
| LMArena WebDev: React | 1395 | 1478 | MiniMax M3 |
| LMArena WebDev: HTML | 1422 | 1455 | MiniMax M3 |
| LMArena WebDev: from an image | 1443 | 1470 | MiniMax M3 |
| Neither model has a result on the other 13 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.3-Codex | MiniMax M3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 32.5 | 29.2 | GPT-5.3-Codex |
| Epoch Capabilities Index | 157.1 | 146.9 | GPT-5.3-Codex |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index15
| Benchmark | GPT-5.3-Codex | MiniMax M3 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 29.5 | Only MiniMax M3 measured |
| Design Arena: agents building web apps | 1033 | 1200 | MiniMax M3 |
| Design Arena: agents building full-stack apps | 983 | 1197 | MiniMax M3 |
| Design Arena: agents building mobile apps | 1054 | 1169 | MiniMax M3 |
| Design Arena: agents building Android apps | 1084 | 1164 | MiniMax M3 |
| Design Arena: agents building Godot games | 1124 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building games | Not measured | 1158 | Only MiniMax M3 measured |
| Design Arena: HTML slides | Not measured | 1180 | Only MiniMax M3 measured |
| Design Arena: PowerPoint slides | Not measured | 1205 | Only MiniMax M3 measured |
| Design Arena: spreadsheets | Not measured | 1021 | Only MiniMax M3 measured |
| LiveBench research | Not measured | 68.0% | Only MiniMax M3 measured |
| LiveBench browser | Not measured | 8.0% | Only MiniMax M3 measured |
| LiveBench long agentic | Not measured | 12.4% | Only MiniMax M3 measured |
| Finance Agent (Vals AI) | Not measured | 48.3% | Only MiniMax M3 measured |
| Tax Agent Bench (Vals AI) | Not measured | 20.6% | Only MiniMax M3 measured |
| Legal Research Bench (Vals AI) | Not measured | 29.8% | Only MiniMax M3 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 47.8% | Only MiniMax M3 measured |
| τ²-Bench Telecom | 86.0% | 88.9% | MiniMax M3 |
| APEX-Agents | Not measured | 37.7% | Only MiniMax M3 measured |
| METR time horizons | 74.5% | Not measured | Only GPT-5.3-Codex measured |
| OSWorld 2.0 | Not measured | 4.6% | Only MiniMax M3 measured |
| Vending-Bench 2 | $5,940 | $2,158 | GPT-5.3-Codex |
| Neither model has a result on the other 17 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GPT-5.3-Codex | MiniMax M3 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 92.7% | Only MiniMax M3 measured |
| LiveBench reasoning | Not measured | 74.5% | Only MiniMax M3 measured |
| MMMU Pro (Vals AI) | Not measured | 81.2% | Only MiniMax M3 measured |
| GPQA Diamond (Artificial Analysis run) | 91.5% | 92.9% | MiniMax M3 |
| Humanity's Last Exam (Artificial Analysis run) | 42.5% | 39.0% | GPT-5.3-Codex |
| GPQA Diamond (Epoch AI run) | Not measured | 90.9% | Only MiniMax M3 measured |
| SimpleBench | Not measured | 45.8% | Only MiniMax M3 measured |
| Chess puzzles | Not measured | 13.0% | Only MiniMax M3 measured |
| Mystery game puzzles | Not measured | 8.0% | Only MiniMax M3 measured |
| LMArena Text: hard prompts | Not measured | 1447 | Only MiniMax M3 measured |
| LMArena Text: expert | Not measured | 1461 | Only MiniMax M3 measured |
| LMArena Text: hard prompts in English | Not measured | 1455 | Only MiniMax M3 measured |
| LMArena Vision: diagrams | Not measured | 1266 | Only MiniMax M3 measured |
| LMArena Vision: homework | Not measured | 1268 | Only MiniMax M3 measured |
| Neither model has a result on the other 7 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-5.3-Codex | MiniMax M3 | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 71.1% | Only MiniMax M3 measured |
| ProofBench | Not measured | 18.0% | Only MiniMax M3 measured |
| LMArena Text: math | Not measured | 1429 | Only MiniMax M3 measured |
| LMArena Text: mathematical work | Not measured | 1448 | Only MiniMax M3 measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | GPT-5.3-Codex | MiniMax M3 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 84.2% | Only MiniMax M3 measured |
| LegalBench (Vals AI) | Not measured | 85.4% | Only MiniMax M3 measured |
| CorpFin (Vals AI) | Not measured | 68.1% | Only MiniMax M3 measured |
| TaxEval (Vals AI) | Not measured | 72.7% | Only MiniMax M3 measured |
| MortgageTax (Vals AI) | Not measured | 68.4% | Only MiniMax M3 measured |
| MedCode (Vals AI) | Not measured | 46.3% | Only MiniMax M3 measured |
| LMArena Text: science | Not measured | 1452 | Only MiniMax M3 measured |
| LMArena Text: medicine and healthcare | Not measured | 1441 | Only MiniMax M3 measured |
| LMArena Text: legal and government | Not measured | 1437 | Only MiniMax M3 measured |
| LMArena Text: business and finance | Not measured | 1434 | Only MiniMax M3 measured |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index08
| Benchmark | GPT-5.3-Codex | MiniMax M3 | Leader |
|---|---|---|---|
| Design Arena: websites | 1163 | 1257 | MiniMax M3 |
| Design Arena: UI components | 1137 | 1246 | MiniMax M3 |
| Design Arena: game dev | 1162 | 1210 | MiniMax M3 |
| Design Arena: data viz | 1164 | 1235 | MiniMax M3 |
| Design Arena: 3D | 1008 | 1209 | MiniMax M3 |
| Design Arena: all code categories | 1145 | 1247 | MiniMax M3 |
| Design Arena: SVG | 1129 | 1161 | MiniMax M3 |
| Design Arena: ASCII art | 1151 | 1161 | MiniMax M3 |
| LiveBench design | Not measured | 66.2% | Only MiniMax M3 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GPT-5.3-Codex | MiniMax M3 | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 76.2% | Only MiniMax M3 measured |
| Public Benefits Bench (Vals AI) | Not measured | 64.1% | Only MiniMax M3 measured |
| SAGE (Vals AI) | Not measured | 50.6% | Only MiniMax M3 measured |
| MedScribe (Vals AI) | Not measured | 87.3% | Only MiniMax M3 measured |
| Vals Multimodal Index | Not measured | 60.0% | Only MiniMax M3 measured |
| IFBench | 75.4% | 82.9% | MiniMax M3 |
| Long context reasoning (AA-LCR) | 83.3% | 83.0% | GPT-5.3-Codex |
| DTBench | Not measured | 78.9% | Only MiniMax M3 measured |
| LMCA | Not measured | 33.7% | Only MiniMax M3 measured |
| Surface Evolver Bench | Not measured | 55.0% | Only MiniMax M3 measured |
| LMArena Text | Not measured | 1433 | Only MiniMax M3 measured |
| LMArena Text: instruction following | Not measured | 1433 | Only MiniMax M3 measured |
| LMArena Text: creative writing | Not measured | 1404 | Only MiniMax M3 measured |
| LMArena Vision | Not measured | 1251 | Only MiniMax M3 measured |
| LMArena Text: English | Not measured | 1443 | Only MiniMax M3 measured |
| LMArena Text: non-English | Not measured | 1420 | Only MiniMax M3 measured |
| LMArena Text: Chinese | Not measured | 1466 | Only MiniMax M3 measured |
| LMArena Text: German | Not measured | 1428 | Only MiniMax M3 measured |
| LMArena Text: Japanese | Not measured | 1380 | Only MiniMax M3 measured |
| LMArena Text: Korean | Not measured | 1373 | Only MiniMax M3 measured |
| LMArena Text: French | Not measured | 1444 | Only MiniMax M3 measured |
| LMArena Text: Spanish | Not measured | 1433 | Only MiniMax M3 measured |
| LMArena Text: Russian | Not measured | 1426 | Only MiniMax M3 measured |
| LMArena Text: Polish | Not measured | 1423 | Only MiniMax M3 measured |
| LMArena Text: multi-turn | Not measured | 1443 | Only MiniMax M3 measured |
| LMArena Text: longer queries | Not measured | 1446 | Only MiniMax M3 measured |
| LMArena Text: writing and language | Not measured | 1419 | Only MiniMax M3 measured |
| LMArena Text: entertainment and media | Not measured | 1403 | Only MiniMax M3 measured |
| LMArena Vision: English | Not measured | 1263 | Only MiniMax M3 measured |
| LMArena Vision: Chinese | Not measured | 1311 | Only MiniMax M3 measured |
| LMArena Vision: reading text | Not measured | 1259 | Only MiniMax M3 measured |
| LMArena Vision: humor | Not measured | 1235 | Only MiniMax M3 measured |
| LMArena Vision: recognizing things | Not measured | 1197 | Only MiniMax M3 measured |
| LMArena Vision: creative writing | Not measured | 1234 | Only MiniMax M3 measured |
| LMArena Document | Not measured | 1435 | Only MiniMax M3 measured |
| Neither model has a result on the other 11 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.3-Codex | MiniMax M3 | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
MiniMax M3 costs less
Repository review
50K input + 3K output tokens
MiniMax M3 costs less
Agent loop
200K input + 10K output tokens
MiniMax M3 costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | OpenAI GPT-5.3-Codex | MiniMax M3 |
|---|---|---|
| Maker | OpenAI | MiniMax |
| Weights | No public weights listed | Open weights |
| Released | February 24, 2026 | May 31, 2026 |
| Context window | 400K | 1.05M |
| Input $/Mtok | $1.75 | $0.30 |
| Output $/Mtok | $14.00 | $1.20 |
| Coding index | 29.5 | 57.8 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | $0.59 | $0.56 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.3-Codex better than MiniMax M3 for coding?
Pick MiniMax M3 for coding: it scores 57.8 on the Vibe Coding Index against 29.5 for GPT-5.3-Codex. Pick GPT-5.3-Codex when what matters is LiveCodeBench (Vals AI) or SWE-bench Verified (Vals AI), where it leads.
Which is cheaper, GPT-5.3-Codex or MiniMax M3?
GPT-5.3-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; MiniMax M3 costs $0.30 and $1.20. Per solved SWE-bench Verified task, GPT-5.3-Codex costs $0.59 and MiniMax M3 $0.56.
Which has the bigger context window, GPT-5.3-Codex or MiniMax M3?
MiniMax M3, with 1.05M tokens against 400K.
How many benchmarks is the GPT-5.3-Codex vs MiniMax M3 comparison based on?
29 benchmarks have a result for both models as of October 21, 2026. GPT-5.3-Codex's coding index rests on 1 independent benchmark and MiniMax M3's on 2. Where only one model has a result, the page says so instead of naming a winner.